Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	120,998
2	South Dakota	111,591
3	Iowa	88,767
4	Wisconsin	88,661
5	Nebraska	85,663
6	Utah	84,823
7	Rhode Island	83,020
8	Tennessee	82,946
9	Idaho	78,424
10	Kansas	77,355
11	Wyoming	76,254
12	Montana	76,116
13	Illinois	75,589
14	Indiana	75,454
15	Arkansas	73,705
16	Minnesota	73,261
17	Alabama	72,773
18	Oklahoma	72,537
19	Nevada	72,409
20	Mississippi	71,587
21	Arizona	70,804
22	Missouri	67,467
23	New Mexico	67,348
24	Louisiana	66,948
25	Alaska	63,210
26	Florida	60,812
27	Texas	60,535
28	Kentucky	59,984
29	Georgia	59,605
30	Ohio	59,093
31	South Carolina	58,941
32	Delaware	58,120
33	Colorado	57,770
34	California	57,589
35	New Jersey	53,397
36	Massachusetts	53,379
37	Michigan	52,762
38	Connecticut	51,514
39	North Carolina	51,021
40	Pennsylvania	49,735
41	New York	49,535
42	West Virginia	46,996
43	Maryland	45,301
44	District of Columbia	40,748
45	Virginia	40,342
46	Puerto Rico	34,287
47	Washington	32,496
48	New Hampshire	31,802
49	Oregon	26,625
50	Maine	17,481
51	Hawaii	15,032
52	Vermont	11,660

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Rhode Island	1,851
2	Kansas	1,394
3	Connecticut	1,020
4	Arizona	956
5	California	913
6	Tennessee	837
7	Arkansas	834
8	Louisiana	825
9	Alabama	760
10	Mississippi	746
11	Massachusetts	734
12	Utah	734
13	Texas	733
14	Idaho	721
15	Georgia	699
16	Kentucky	684
17	Oklahoma	664
18	West Virginia	653
19	Nebraska	604
20	New York	604
21	New Hampshire	596
22	Pennsylvania	583
23	Nevada	582
24	Ohio	576
25	North Carolina	561
26	Indiana	553
27	Wyoming	537
28	Missouri	536
29	Florida	529
30	Delaware	526
31	New Mexico	516
32	Iowa	503
33	South Dakota	501
34	Montana	490
35	Illinois	478
36	South Carolina	472
37	Wisconsin	437
38	Virginia	420
39	New Jersey	417
40	Maine	401
41	Michigan	379
42	Colorado	371
43	Maryland	354
44	North Dakota	321
45	Alaska	316
46	Washington	280
47	Puerto Rico	277
48	District of Columbia	262
49	Minnesota	239
50	Oregon	202
51	Vermont	131
52	Hawaii	51

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,133
2	New York	1,923
3	Massachusetts	1,790
4	North Dakota	1,691
5	Rhode Island	1,677
6	Connecticut	1,672
7	South Dakota	1,654
8	Louisiana	1,602
9	Mississippi	1,595
10	Illinois	1,405
11	Michigan	1,302
12	Pennsylvania	1,227
13	Indiana	1,212
14	Iowa	1,211
15	Arkansas	1,205
16	Arizona	1,198
17	New Mexico	1,162
18	District of Columbia	1,105
19	South Carolina	1,019
20	Florida	1,003
21	Nevada	1,000
22	Georgia	989
23	Tennessee	988
24	Alabama	973
25	Maryland	967
26	Texas	959
27	Missouri	952
28	Delaware	945
29	Minnesota	943
30	Kansas	940
31	Wisconsin	892
32	Montana	888
33	Nebraska	847
34	Colorado	837
35	Idaho	788
36	Ohio	757
37	West Virginia	735
38	Wyoming	699
39	Kentucky	658
40	North Carolina	646
41	California	642
42	Oklahoma	619
43	Virginia	583
44	New Hampshire	544
45	Puerto Rico	464
46	Washington	457
47	Utah	392
48	Oregon	350
49	Alaska	266
50	Maine	248
51	Vermont	214
52	Hawaii	199

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Kansas	26
2	Rhode Island	22
3	Wyoming	18
4	Arkansas	17
5	Pennsylvania	17
6	Connecticut	16
7	Indiana	15
8	Mississippi	15
9	New Mexico	14
10	Tennessee	14
11	Arizona	13
12	Illinois	12
13	Nevada	12
14	New Jersey	12
15	Louisiana	11
16	Massachusetts	11
17	West Virginia	11
18	Michigan	10
19	Missouri	10
20	California	9
21	Colorado	9
22	Idaho	9
23	Nebraska	9
24	Ohio	9
25	Delaware	8
26	Iowa	8
27	Montana	8
28	New Hampshire	8
29	Texas	8
30	Washington	8
31	Wisconsin	8
32	Kentucky	7
33	Maryland	7
34	New York	7
35	Minnesota	6
36	North Dakota	6
37	Oklahoma	6
38	South Carolina	6
39	South Dakota	6
40	Alabama	5
41	District of Columbia	5
42	Florida	5
43	North Carolina	5
44	Virginia	5
45	Georgia	4
46	Puerto Rico	4
47	Utah	4
48	Oregon	3
49	Vermont	3
50	Maine	2
51	Alaska	1
52	Hawaii	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	270,582	1	99
Norton	Kansas	219,922	2	99
Lincoln	Arkansas	215,756	3	99
Dewey	South Dakota	214,189	4	99
Bon Homme	South Dakota	214,027	5	99
Davidson	Tennessee	97,450	333	89
Richland	South Carolina	62,656	1596	49
York	South Carolina	53,168	2075	33
Orange	California	51,954	2125	32
Pierce	Washington	30,240	2832	9

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	7,587	1	99
Jerauld	South Dakota	7,452	2	99
Dickey	North Dakota	6,568	3	99
Gregory	South Dakota	6,213	4	99
Foster	North Dakota	5,919	5	99
Davidson	Tennessee	813	1812	42
Richland	South Carolina	803	1828	41
York	South Carolina	634	2127	32
Orange	California	590	2203	29
Pierce	Washington	382	2573	18

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.